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langgraph-python-quickstart

该 Skill 引导按 LangChain 快速入门创建独立的 LangGraph Python 项目：先询问用户选择模型提供商，再生成计算器/数学智能体代码、配置 .env、安装依赖并运行示例。它优先使用 Graph API，跳过图可视化，并限定不额外集成 LangSmith 或 Tavily。

langchain-ai
热度 64.68
最近更新 2026-10-19 02:34:07
分类：开发依赖：代码执行 / 读写文件 / 第三方 API / MCP 服务 / 内部调用大模型应用场景：代码生成 / API 集成 / 工作流

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add langchain-ai/langchain-skills/langgraph-python-quickstart
Metadata
name
langgraph-python-quickstart
description
Scaffold a minimal local LangGraph agent in Python by following the official quickstart. Use when the user wants to quickly build or try a LangGraph agent locally.

LangGraph Python quickstart

Follow the live docs — do not invent an alternate API from memory:

https://docs.langchain.com/oss/python/langgraph/quickstart

Fetch that page (Docs MCP or HTTP) and implement what it shows (calculator / math agent with the Graph API). Prefer the Graph API path over the Functional API unless the user asks otherwise. Skip IPython graph visualization.

Local setup constraints

Apply these on top of the quickstart (they keep setup minimal and model-agnostic):

  1. Ask which provider/model to use. Showcase that LangGraph works with any LangChain chat model. Suggested prompt:

    Which model should this agent use? Pass a provider:model string — e.g. openai:gpt-5.5, anthropic:claude-sonnet-5, google_genai:gemini-2.5-flash-lite. Default if you're unsure: anthropic:claude-sonnet-5.

    The docs often hardcode Anthropic — replace with init_chat_model("<MODEL>") (or equivalent) using their choice. If using Claude Sonnet 5+, omit temperature / top_p / top_k (unsupported).

  2. Create a new directory (e.g. langgraph-agent/) and do all work there — do not pollute the open project.

  3. Only secret: the provider API key in .env (gitignored). No LangSmith / Tavily unless they ask. Prefer they edit .env themselves — don't paste keys into chat.

  4. Install packages from the quickstart plus the provider package for their model.

  5. Run the example (e.g. “Add 3 and 4.”), show output, then stop. Point to langgraph-fundamentals for next steps. For a higher-level agent API, use LangChain create_agent instead.